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Evaluation of automatic atlas-based lymph node segmentation for head-and-neck cancer
Liza J Stapleford1, Joshua D Lawson, Charles Perkins
1Department of Radiation Oncology, Emory University School of Medicine and Winship Cancer Institute of Emory University, Atlanta, GA 30322, USA.
Purpose:
To evaluate if automatic atlas-based lymph node segmentation (LNS) improves efficiency and decreases inter-observer variability while maintaining accuracy.
Methods And Materials:
Five physicians with head-and-neck IMRT experience used computed tomography (CT) data from 5 patients to create bilateral neck clinical target volumes covering specified nodal levels. A second contour set was automatically generated using a commercially available atlas. Physicians modified the automatic contours to make them acceptable for treatment planning. To assess contour variability, the Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm was used to take collections of contours and calculate a probabilistic estimate of the "true" segmentation. Differences between the manual, automatic, and automatic-modified (AM) contours were analyzed using multiple metrics.
Results:
Compared with the "true" segmentation created from manual contours, the automatic contours had a high degree of accuracy, with sensitivity, Dice similarity coefficient, and mean/max surface disagreement values comparable to the average manual contour (86%, 76%, 3.3/17.4 mm automatic vs. 73%, 79%, 2.8/17 mm manual). The AM group was more consistent than the manual group for multiple metrics, most notably reducing the range of contour volume (106-430 mL manual vs. 176-347 mL AM) and percent false positivity (1-37% manual vs. 1-7% AM). Average contouring time savings with the automatic segmentation was 11.5 min per patient, a 35% reduction.
Conclusions:
Using the STAPLE algorithm to generate "true" contours from multiple physician contours, we demonstrated that, in comparison with manual segmentation, atlas-based automatic LNS for head-and-neck cancer is accurate, efficient, and reduces interobserver variability.
Insights
Automatic atlas-based lymph node segmentation (LNS) for head and neck cancer is accurate and efficient. This method reduces inter-observer variability compared to manual segmentation, saving time in treatment planning.
Area of Science:
- Medical imaging and radiation oncology.
- Computational anatomy and image analysis.
Background:
- Accurate lymph node segmentation (LNS) is crucial for effective head and neck cancer radiotherapy.
- Manual segmentation is time-consuming and prone to inter-observer variability.
Purpose of the Study:
- To assess the accuracy, efficiency, and inter-observer variability of automatic atlas-based LNS compared to manual methods.
- To determine if automatic LNS improves treatment planning efficiency.
Main Methods:
- Five physicians manually contoured lymph node volumes on CT scans from 5 patients.
- Automatic contours were generated using an atlas-based approach and subsequently modified by physicians.
- The Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm was used to establish a reference "true" segmentation.
Main Results:
- Automatic contours showed high accuracy, comparable to manual segmentation (e.g., 76% Dice similarity coefficient).
- Automatic-modified contours significantly reduced contour volume range and false positivity compared to manual contours.
- Average time savings of 11.5 minutes per patient (35% reduction) were achieved with automatic segmentation.
Conclusions:
- Atlas-based automatic LNS for head and neck cancer is accurate and efficient.
- This automated approach effectively reduces inter-observer variability in contouring.
- Automatic LNS offers a promising improvement over manual segmentation for treatment planning.
